Calibrated Trust
Adjusting your verification level based on code stakes and AI capabilities.
Simple Definition
Calibrated Trust means adjusting how carefully you review AI-generated code based on what's at stake. High-stakes code (auth, payments) gets maximum scrutiny. Low-stakes code (formatting, boilerplate) gets basic review.
Technical Definition
A risk-based approach to AI output verification:
| Code Stakes | Verification Level | Examples |
|---|---|---|
| High | Maximum: security scanning, expert review, full test coverage | Auth, crypto, compliance, financial |
| Medium | Standard: thorough review, domain-aware reviewer | Business logic, APIs, data handling |
| Low | Basic: quick review, linting, unit tests | Boilerplate, formatting, CRUD |
Why Not Just Trust Everything (or Nothing)?
Trusting everything (vibe coding): 45% of AI code has vulnerabilities in controlled testing. Blind trust leads to security breaches.
Trusting nothing: Wastes expert capacity on low-stakes work. If you manually verify every line of boilerplate, you burn review budget that should go to critical code.
Calibrated trust: Direct maximum verification to maximum-risk code. Use basic verification for low-risk code. This is how human code review already works—AI code should follow the same pattern.
Key Takeaways
- Calibrated trust = verification proportional to risk
- High-stakes code (auth, payments) needs maximum review
- Low-stakes code (boilerplate) needs basic review
- Both over-trust and under-trust waste resources